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design and product quality evolution
International Journal of Refrigeration 28 (2005) 471–480 www.elsevier.com/locate/ijrefrig
A continuous/discrete simulation of controlled atmosphere (CA) cool storage systems: evaluation of plant performance/design and product quality evolution H.B. Nahora,*, N. Scheerlincka, P. Verbovena, J. Van Impeb, B.M. Nicolaı¨a a
Flanders Center/Laboratory of Postharvest Technology, Department of Agro-Engineering and -Economics, Katholieke Universiteit Leuven, W. de Croylaan 42, B-3001 Leuven, Belgium b Bioprocess Technology and Control, Department of Chemical Engineering, Katholieke Universiteit Leuven, W. de Croylaan 46, B-3001 Leuven, Belgium Received 1 August 2004; received in revised form 18 November 2004; accepted 30 November 2004 Available online 20 January 2005
Abstract As an application of the CA cool storage simulation model developed and validated earlier, the implications of different product loading strategies (batch-wise and step-wise) on the product quality, mechanical plants performance and design aspects were investigated by considering a hypothetical CA cool storage facility. The batch-wise product loading was found to be advantageous with regards to firmness loss of the produce but was marginally energy intensive over the step-wise product loading strategy. Moreover, the step-wise loading scheme required low capacity individual evaporators and higher plant capacity of the gas-handling unit (N2 generator and CO2 scrubber), as compared to the batch-wise product loading. It was demonstrated that using the model, appraisal of the implication of practical industrial operational procedures such as product loading strategy, on product quality and plant performance/design was possible, owing to the discrete continuous modeling approach. q 2004 Elsevier Ltd and IIR. All rights reserved. Keywords: Refrigerated storage; Controlled atmosphere; Modelling; Heat balance; Heat transfer; Performance
Simulation continue/nume´rique de syste`mes d’entreposage frigorifique a` atmosphe`re controˆle´e: e´valuation de la performance ainsi que la conception des intallations et l’e´volution de la qualite´ des produits Mots cle´s : Entreposage frigorifique ; Atmosphe`re controˆle´e ; Mode´lisation ; Bilan thermique ; Transfert de chaleur ; Performance
1. Introduction * Corresponding author. Tel.: C32 16 32 26 68; fax: C32 16 32 29 55. E-mail addresses: [email protected] (H.B. Nahor), [email protected] (H.B. Nahor). 0140-7007/$35.00 q 2004 Elsevier Ltd and IIR. All rights reserved. doi:10.1016/j.ijrefrig.2004.11.010
In most west European countries, cool storage facilities for fruit and vegetables are owned by growers’ cooperatives. Assumed to suite operational and fluctuating market conditions during selling of the fruit, various customs of
472
H.B. Nahor et al. / International Journal of Refrigeration 28 (2005) 471–480
Nomenclature Crefr q p hCOP
cost of refrigeration, V tonK1 chilling and storage heat load per ton of stored produce, kJ tonK1 price of electricity, V kWhK1 coefficient of performance of the whole cooling plant
loading of the cool cells are practiced. In general, two loading strategies can be identified: (i) ‘batch-wise’ and (ii) ‘step-wise’ loading of the cells. In the batch-wise loading strategy, each batch arriving in the station is pushed into a cool cell until it is full to its capacity and the process continues to the next cell and so forth until the available cells are filled. This type of loading strategy is practiced, for instance, in facilities owned by growers’ cooperatives in France. On the other hand, in step-wise loading, the arriving produce is distributed into the available or allocated cool cells in the facility and the cells are progressively filled as the customers (growers) bring in their produce. This allows to fill the cool cells almost at the same time and can take up to 1 month to fill the allocated cells. This type of loading strategy is pursued by the installations owned by the growers’ cooperatives in Belgium. In this paper, the validated CA cool storage model [1] will be used to investigate different working conditions and their influence on final product quality and mechanical plant design/performance by considering these customs of product loading schemes using a hypothetical CA cool storage facility.
2. Hypothetical cool storage facility Here, in order to investigate the implication of the step-
Fig. 1. Hypothetical step-wise product loading profile.
wise and batch-wise loading strategies, a hypothetical industrial CA cool storage facility with 10 ULO (UltraLow Oxygen) cells was considered. The dimensions of the cells and the cooling capacity of the evaporators in the cells were assumed to be the same as the one described in Nahor et al. [1] with a total capacity of 430 bins of size 1.2!1.0! 0.75 m3, filled with Jonagold apples. It was assumed that 430 bins full of product arrive at the facility every other day for storage. Then, in step-wise loading, the bins were distributed to the 10 cool cells, and the cells were filled at the end of day 18. This means 43 bins were loaded in each cell every other day. The profile of such loading scheme is shown in Fig. 1. In the batch-wise loading, a single cell was loaded to its capacity at a time, as the produce arrives. That is, all the 430 bins were loaded to a single cell and the next batches were loaded to the next cell and so forth until all the cells were filled. The products were assumed to be introduced at a uniform temperature of 10 8C and all the cells were at the set point temperature of 1 8C. In both cases, the ULO operation starts when a cell is full and 2 days after storage product temperature is attained. This means that for the step-wise loading the operation will start at almost the same time for all the cells. While for the batch-wise loading, each cell was ‘burned’ as the above stated conditions were fulfilled for that cell. For comparison purposes, cost of energy of refrigeration will be calculated using the following formula.
Crefr Z
qp hCOP
(1)
where Crefr is the cost of refrigeration, V tonK1; q, the chilling and storage heat load per ton of stored produce, kJ tonK1; p, the price of electricity (Z0.08 V kWhK1) and hCOP the coefficient of performance of the whole refrigeration plant which was assumed to be 2.51. Moreover, in order to quantify the firmness loss of the apples in terms of money, the grading system as shown in Fig. 2 was defined. This was based on the ‘Belgische Fruitveiling’ quality grading system for Jonagold apples [3]. In this grading, the symbols A1, A2, A3 and A4 represent apples with 50, 33, 25 and 20% red colour, respectively. On the other hand, the CC, C, Y and R represent the base (background) colour and the firmness of the fruit, CC being the extremely green base and extremely firm apple and R the yellow and soft apple. The simulation was carried out on a Celeron 366 Mhz, 128 Mb Ram personal computer with Microsoft Windows NT OS. 1 This value was estimated from data obtained from 5 refrigeration installations for fruit and vegetables in Belgium. The value corresponds to the one given in Hasse et al. [2] (Z2.56).
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Fig. 2. Quality grading system based on appearance and firmness (A1, at least 50% red colour; A2, at least 33% red colour; A3, at least 25% red colour; A4, at least 20% red colour; CC, extremely green base and extremely firm; C, greenish-yellow and firm; Y, yellow; R, ripe).
3. Results 3.1. Effect of loading strategy on product quality 3.1.1. Product cooling rate In Fig. 3, the product temperature profiles and their respective room air temperatures are depicted for the two loading strategies. For the case of the full load condition
(batch-wise loading), the air temperature was observed to rise, since the load imposed by the product was higher than the available cooling capacity of the specific cell. On the other hand, for the step-wise loading (i.e. 10% load condition) the effect of the produce was barely evident on the room temperature. Obviously, slower product cooling rate is expected in the fully loaded condition. However, in comparison, in cooling 10% of the full load condition the
Fig. 3. (a) Typical product profiles in one of the cells of the batch-wise and step-wise loading conditions (b) corresponding room air temperatures (BW, batch-wise; SW, step-wise).
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Fig. 4. (a) Accumulated weight loss of the batch-wise and the step-wise loading conditions and (b) their respective room air relative humidity’s (BW, batch-wise; SW, step-wise).
apparent gain in cooling rate was only less than 50%. One reason for this could be that the heat transfer coefficient was considered constant and the same in both cases. Nevertheless, due to higher flow resistance in the fully loaded condition, a smaller heat transfer coefficient is expected resulting in slower cooling rate that the observed one. Another reason is the heat transfer limitation; i.e. even
though there was extra cooling capacity able to take more heat load in the case of the partially loaded condition, the rate of heat removal from the product was the limiting factor. Thus, the control system tries to keep the room temperature at the required set point by controlling the cooling capacity via the control valve. It should be noted here that the cooling rate obtained for the full load condition
Fig. 5. (a) Firmness loss of stored produce of the first cell of the batch-wise and in one of the cells of the step-wise loading schemes (b) their respective respiratory gas concentration profiles (BW, batch-wise; SW, step-wise).
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Fig. 6. (a) Heat load on the refrigeration plant with 10 ULO cells for batch-wise and the step-wise loading conditions and (b) their respective product heat load (BW, batch-wise; SW, step-wise).
was comparatively more dependent on the available capacity of the individual evaporators in each room. Nonetheless, from the product point of view the step-wise loading appears to be advantageous as it provides faster cooling. 3.1.2. Product weight loss The accumulated weight loss and their respective room air relative humidity are depicted in Fig. 4 for both strategies. A higher rate of moisture loss was observed for the full load condition at the beginning, because of low room air relative humidity as well as high temperature. Once at steady state, a constant rate moisture loss was attained. On the other hand, for the step-wise loading, a slower rate of moisture loss was observed at the beginning. However, due to the reduced relative humidity, each time product was loaded, a higher rate of moisture loss was observed until the end of product loading and stabilizes to a constant rate similar to the full load condition. When extended to a storage period of 180 days, 2.5 and 2.3% cumulative moisture loss was predicted for the step-wise and batch-wise loading strategies, respectively. This slight difference, which can hardly be explained, was evidently much less than the prediction accuracy of the employed models. These values correspond with the reported moisture loss of 2.5– 3.0% [4] in practice for Jonagold apples. No apparent advantage of one strategy over the other was observed in relation to moisture loss, as both stabilize to the same rate of moisture loss. 3.1.3. Product firmness loss Fig. 5 shows the firmness loss of the stored produce of
the two loading strategies and their respective room air concentrations of the respiratory gases. The advantage of the batch-wise over the step-wise loading was that once the room was full and once it attains the product set point temperature the ‘burning’ operation can start (in practice this is about 5–10 days after filling depending on the size of the cool cells). While in the step-wise loading, the ‘burning’ operation cannot start until all the cells were full. The observed difference in firmness was the maximum that can occur, that is the difference in firmness between the first batch loaded in the batch-wise and step-wise schemes. This difference closes to zero for the last batch, as in both cases the ‘burning’ operation started at about the 25th day after the start of loading of the first batch. It should be noted that the step-wise loading may result in difference in quality attributes between the first loaded and the last loaded product due to the longer time the first loaded product spends under atmospheric air with respect to the last loaded product. Overall, considering the whole produce and the biological variability (and thus the accuracy of the model) the observed difference may not be significant. However, as it is proven that fruit stored under optimal respiratory gas concentrations exhibit higher firmness than in normal air, this difference may be significant when lengthy step-wise loading periods are considered. Hence, it might be interesting to quantify the financial implication of the observed difference. At storage time of 120 days (4 months), the firmness of the first batch of the batch-wise-loaded fruit was around 60 N and that of the step-wise-loaded, 57 N. It is evident that there is a strong correlation between the firmness and the background colour of the fruit. Hence,
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Fig. 7. (a) Cumulative cooling and storage heat for the batch-wise and the step-wise loading conditions and (b) their respective cost per ton of product (BW, batch-wise; SW, step-wise).
there is a fair chance that these fruit lie in different quality groups, for instance in the high price category (500– 550 V tonK1) and the second level price category (450– 475 V tonK1) (Fig. 2). This means a difference in selling price of 50–75 V per ton. Assuming a cool cell contained 130 tons of fruit an apparent loss of 6500 V per cell is incurred. 3.2. Effect of loading strategy on mechanical plants’ performance/design 3.2.1. Refrigeration unit The effect of the product loading strategy on the refrigeration unit is depicted in Fig. 6(a). In the case of the batch-wise product loading condition, the heat load imposed on the refrigeration unit was delivered from the cell(s) that were packed to their capacity (product heat load and losses) and the rest of the cells to maintain their room air temperature set point (losses). In this case, less load fluctuation was observed as compared to the step-wise loading scheme where the heat load comes from the 10 partially filled rooms. This is because of the fact that in the case of step-wise loading, instantaneous high capacity requirement was demanded from all the cells operating in parallel while the plant was left idle on other instances. The major heat load was imposed by the product field heat (latent and sensible) and respiration heat. In Fig. 6(b), the product heat load component is shown for the two cases. In the case of the batch-wise loading condition, a peak and drastic decrease was observed at each loading event. This drastic decrease at the beginning was caused by the heating
up of the room temperature, thus decreasing the driving force, and resulting in a lower heat load. Subsequently, a slower rate of decrease was observed as compared to the step-wise loading condition. In order to estimate the energy usage by the refrigeration unit, the area under the heat load curves (Fig. 6(a)) was calculated. This is depicted in Fig. 7(a) where the evolution of the cumulative energy usage during the storage period is shown. This comparison shows that operationally the step-wise loading appears to have marginal lower energy cost. In Fig. 7(b) the cost of cooling and storing is shown per ton of produce. In the case of batchwise loading, it was observed that there was a large increase in the cost per ton of produce at the beginning and afterwards a gentle increase as more and more products were loaded. This shows that the load imposed on the refrigeration unit at the beginning was higher compared to the step-wise loading. It was observed that if extrapolated to 180 days of storage, there exists only a marginal difference (!1 V tonK1) in the total cost to cool and store 1 metric ton of produce (Jonagold apple in this case), which amounts to G45 V tonK1. From the design point of view, the available refrigeration capacity for the step-wise loading scheme was not efficiently used, as the plant remains idle for most of the time and thus operating at sub-optimal conditions. This scenario was caused by the excess capacity of the individual evaporators (coolers) in the cool cells, which suggests a smaller capacity coolers could be proposed implying smaller capacity refrigeration plant as a whole. On the other hand, in the case of the batch-wise loading, though the unit was not idling, the maximum capacity could not be
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Fig. 8. Individual cell refrigeration loads for (a) batch-wise loading and (b) step-wise loading conditions (BW, batch-wise; SW, step-wise).
utilized. Given that the cooling rate depicted in Fig. 3(a) is to be maintained, the individual capacity of the cooler should be retained. As can be read from Fig. 8(a), during the first 2 days the maximum capacity of the cooler was employed. However, since the load was distributed in time, taking only one cell at a time, again, implies a smaller capacity refrigeration unit.
3.2.2. Gas handling unit 3.2.2.1. Nitrogen generation unit. In Fig. 9, the nitrogen requirement for the ‘burning’ operation in each case of the loading schedule with their respective oxygen concentration profiles is shown. It is to be recalled that the nitrogen generator was attached to a buffer tank from where the
Fig. 9. (a) Nitrogen requirement in batch-wise product loading for 10 cells (b) nitrogen requirement in step-wise loading for 10 cells (c) respective oxygen profiles in the first cell of the batch-wise scheme and in one of the cells in the step-wise scheme (BW, batch-wise; SW, stepwise).
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Fig. 10. (a) Rate of carbon dioxide production for batch-wise and step-wise product loading (b) respective carbon dioxide profiles in the first cell of the batch-wise scheme and in one of the cells in the step-wise scheme (BW, batch-wise; SW, step-wise).
injection to the rooms was carried out. Since, in the case of the step-wise loading the cells are set into ULO operation at the same time, the load on the nitrogen generator is obviously, 10 times (from the 10 cells) higher as compared to batch-wise product loading schedule. In reality, the assumption that the cells get filled at the same time is hardly met. Thus, for instance, the requirement in the step-wise loading can be reduced by 50% if the burning operation of the cells was scheduled in an interval of 10 h instead of at the same time. This means that the ‘burning’ of the last room will start 90 h after the first one (injection time of less than 50 h for each cell as can be seen from Fig. 9(c)), which might have slight but not considerable effect on the firmness loss of the fruit. This shows that the way the cool rooms are loaded has substantial implication on the size of the equipment and thus the investment cost. In design considerations, these two situations depict the required minimum and maximum nitrogen generation capacity and, hence, the demand for nitrogen generation falls between these limits. Practically, the capacity of the nitrogen generation unit is determined by optimising the gas flow rate (99–97% nitrogen) to the cells and the time required to pull down the oxygen concentration to less than 5% (typically 30–50 h). This depends on the size and capacity of the cells, the type of produce and the loading situations. Thus, for a given cell capacity, the optimal gas flow rate and pull-down time, and thus the size of the unit, can be determined by simulating different loading situations and by using various produce using the present tool. 3.2.2.2. Carbon dioxide scrubber. In Fig. 10, the rate of carbon dioxide production in the 10 cells and the respective carbon dioxide concentration profiles for the two product
loading schedules is shown. It is observed that at the end of the loading period (day 20) there exists a difference of more than 120 kgCO2 dK1 in rate of carbon dioxide production. At this moment, more than half of the batch-wise-loaded cells already attained the set point concentrations of the respiratory gases where the rate of respiration was more than 70% slower. When the step-wise-loaded cells were set into ULO operation (day 25), a drastic decrease in carbon dioxide production was observed before it stabilized to the value of 73 kgCO2 dK1 that is the value obtained in batchwise loading, as can be read from Fig. 10(a). This rate was the load the scrubber unit should remove from the cells during the storage period of the fruit. During the transient state of the respiratory gas concentrations, it was observed that for the step-wise loading (cells operate in a parallel mode), a single scrubber could not handle the load causing a long queue and, as a result, considerable build up of carbon dioxide in the cells. Thus, the results as shown in Fig. 10 was obtained by using two scrubbers for the step-wise loading scheme, handling five cells each. However, during the steady state the scrubbers were idling most of the time. In the case of the batch-wise charging, only one scrubber could handle the load with a maximum of three cells being in the queue at times. This means a maximum delay of 40 min (the scrubber needs 20 min per cell, 10 min for scrubbing and 10 for regenerations) and without substantial effect on the concentration of the carbon dioxide in the cool cells. When the total amount of operation time of the scrubber was calculated, it was found 300.67 and 200.33 h (for both scrubbers) for the batch-wise and step-wise schemes, respectively, during the storage period of 40 days. This is understandably so because in the case of batch-wise loading
H.B. Nahor et al. / International Journal of Refrigeration 28 (2005) 471–480 Table 1 Summary of findings Loading strategy Batch-wise Step-wise Product quality (firmness) Energy cost Investment cost
Individual evaporators Gas handling unit
Higher quality product Marginally higher Higher capacity Lower capacity
Lower quality product Marginally lower Lower capacity Higher capacity
the scrubber started working at much earlier time (day 7 as compared to day 25) and was operating one at a time and maintaining the rooms at the set point. This means that during the transient period, the batch-wise scheme incurs higher operational cost. However, it should be emphasised that the cost incurred by the whole gas-handling unit is insignificant as compared to the refrigeration cost (For instance, the adsorbed kW of a unit that can handle 300 kgCO2 dK1 is about 1.8. This means that for the 300.67 h of operation during 40 days, a cost of 0.32 V tonK1 is incurred).
4. Discussion and conclusion As an application of the global CA cool storage model, the implication of some practical operational issues (related to product loading strategies) to the product quality, mechanical plants performance and design aspects was investigated. The summary of the findings are summarized in Table 1. With regards to product quality, it was found that no difference in cumulative moisture loss was observed. However, the batch-wise product loading was apparently advantageous with regards to firmness loss of the produce. In general, the influence of the considered product loading schemes on the product quality was apparent. Concerning the refrigeration plant, less load variation was observed in the batch-wise loading but it was marginally energy intensive over the step-wise product loading strategy. Considering sizing of the refrigeration plant, the batchwise scheme required high capacity individual evaporators as compared to the step-wise loading. However, overall, the capacity of the refrigeration unit is not affected by the product-loading scheme. During transient situations, higher plant capacity was required for the gas-handling unit (nitrogen generator and carbon dioxide scrubber) in the case of the step-wise loading scheme, due to parallel working mode of the cells. Nevertheless, 33% higher operational time was required for the carbon dioxide scrubber when batch-wise product loading strategy was used. In practice, operational and market situations and other
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aspects are considered, where the decision to go for either one of the strategies is based upon. This has to do with the freedom of decision the growers stipulate as to when to sell their produce. When this flexibility is required, the step-wise loading strategy is ideal because (i) the cell(s) a grower hires are not shared and he can load them at his own picking rate and (ii) does not interfere with other growers’ decision and can sell his produce at his own choosing when he thinks the price is profitable. On the other hand, when collective decisions drive the market the batch-wise loading is preferred because of its simplicity in scheduling the available storage space and somewhat easier operational condition with respect to loading of the product. The light this investigation sheds on these choices is that at what cost, besides the operational conditions and flexibility, are these decisions made. From this perspective, the decision can be weighed in its multi-facets of product quality, plant performance and operational and market conditions. Based on that, other loading strategies can be designed which for instance consider the combination of the two strategies, achieved by allocating some of the available storage space to step-wise and the rest to batch-wise product-loading strategies. In conclusion, it was demonstrated that the global CA cool storage model could be used for sizing (design) and evaluation of plant performance taking into consideration the implication on the product quality. Other areas of application include development of new control strategies and tuning of controller parameters and model based product control. Concerning the latter, since sensor based automatic control of product quality can prove to be difficult, the control of the ‘a priori’ known optimal set points can be assisted by model based product quality control. In this case, the response from the quality models is fed to the controller for better maintenance of the microenvironment for overall plant optimal operation and increased final product quality.
Acknowledgements The IDO/00/008 project and the IRO scholarship (KULeuven) are acknowledged for financial assistance. Authors N. Scheerlinck and P. Verboven are postdoctoral fellows with the Flemish Fund for Scientific Research (F.W.O.—Vlaanderen).
References [1] H.B. Nahor, N. Scheerlinck, P. Verboven, J. Van Impe, B.M. Nicolaı¨, A Continuous/discrete simulation of controlled atmosphere (CA) cool storage systems: validation using industrial CA cool storage, Int J Refrigeration, in press, doi:10.1016/j.ijrefrig. 2004.11.009.
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[2] H. Hasse, M. Becker, K. Grossmann, G. Maurer, Top-down model for dynamic simulation of cold-storage plants, Int J Refrigeration 19 (1) (1996) 10–18. [3] www 1. http://www.bfv.be/media/pdf/Brochure%20EN.pdf., accessed on August 30, 2003.
[4] E.A. Veraverbeke, P. Verboven, P. Van Oostveldt, B.M. Nicolai, Prediction of moisture loss across the cuticle of apple (Malus sylvestris subsp. mitis (Wallr.)) during storage. Part 2. Model simulations and practical applications, Postharvest Biol Technol 30 (1) (2003) 89–97.
A continuous/discrete simulation of controlled atmosphere (CA) cool storage systems: evaluation of plant performance/design and product quality evolution H.B. Nahora,*, N. Scheerlincka, P. Verbovena, J. Van Impeb, B.M. Nicolaı¨a a
Flanders Center/Laboratory of Postharvest Technology, Department of Agro-Engineering and -Economics, Katholieke Universiteit Leuven, W. de Croylaan 42, B-3001 Leuven, Belgium b Bioprocess Technology and Control, Department of Chemical Engineering, Katholieke Universiteit Leuven, W. de Croylaan 46, B-3001 Leuven, Belgium Received 1 August 2004; received in revised form 18 November 2004; accepted 30 November 2004 Available online 20 January 2005
Abstract As an application of the CA cool storage simulation model developed and validated earlier, the implications of different product loading strategies (batch-wise and step-wise) on the product quality, mechanical plants performance and design aspects were investigated by considering a hypothetical CA cool storage facility. The batch-wise product loading was found to be advantageous with regards to firmness loss of the produce but was marginally energy intensive over the step-wise product loading strategy. Moreover, the step-wise loading scheme required low capacity individual evaporators and higher plant capacity of the gas-handling unit (N2 generator and CO2 scrubber), as compared to the batch-wise product loading. It was demonstrated that using the model, appraisal of the implication of practical industrial operational procedures such as product loading strategy, on product quality and plant performance/design was possible, owing to the discrete continuous modeling approach. q 2004 Elsevier Ltd and IIR. All rights reserved. Keywords: Refrigerated storage; Controlled atmosphere; Modelling; Heat balance; Heat transfer; Performance
Simulation continue/nume´rique de syste`mes d’entreposage frigorifique a` atmosphe`re controˆle´e: e´valuation de la performance ainsi que la conception des intallations et l’e´volution de la qualite´ des produits Mots cle´s : Entreposage frigorifique ; Atmosphe`re controˆle´e ; Mode´lisation ; Bilan thermique ; Transfert de chaleur ; Performance
1. Introduction * Corresponding author. Tel.: C32 16 32 26 68; fax: C32 16 32 29 55. E-mail addresses: [email protected] (H.B. Nahor), [email protected] (H.B. Nahor). 0140-7007/$35.00 q 2004 Elsevier Ltd and IIR. All rights reserved. doi:10.1016/j.ijrefrig.2004.11.010
In most west European countries, cool storage facilities for fruit and vegetables are owned by growers’ cooperatives. Assumed to suite operational and fluctuating market conditions during selling of the fruit, various customs of
472
H.B. Nahor et al. / International Journal of Refrigeration 28 (2005) 471–480
Nomenclature Crefr q p hCOP
cost of refrigeration, V tonK1 chilling and storage heat load per ton of stored produce, kJ tonK1 price of electricity, V kWhK1 coefficient of performance of the whole cooling plant
loading of the cool cells are practiced. In general, two loading strategies can be identified: (i) ‘batch-wise’ and (ii) ‘step-wise’ loading of the cells. In the batch-wise loading strategy, each batch arriving in the station is pushed into a cool cell until it is full to its capacity and the process continues to the next cell and so forth until the available cells are filled. This type of loading strategy is practiced, for instance, in facilities owned by growers’ cooperatives in France. On the other hand, in step-wise loading, the arriving produce is distributed into the available or allocated cool cells in the facility and the cells are progressively filled as the customers (growers) bring in their produce. This allows to fill the cool cells almost at the same time and can take up to 1 month to fill the allocated cells. This type of loading strategy is pursued by the installations owned by the growers’ cooperatives in Belgium. In this paper, the validated CA cool storage model [1] will be used to investigate different working conditions and their influence on final product quality and mechanical plant design/performance by considering these customs of product loading schemes using a hypothetical CA cool storage facility.
2. Hypothetical cool storage facility Here, in order to investigate the implication of the step-
Fig. 1. Hypothetical step-wise product loading profile.
wise and batch-wise loading strategies, a hypothetical industrial CA cool storage facility with 10 ULO (UltraLow Oxygen) cells was considered. The dimensions of the cells and the cooling capacity of the evaporators in the cells were assumed to be the same as the one described in Nahor et al. [1] with a total capacity of 430 bins of size 1.2!1.0! 0.75 m3, filled with Jonagold apples. It was assumed that 430 bins full of product arrive at the facility every other day for storage. Then, in step-wise loading, the bins were distributed to the 10 cool cells, and the cells were filled at the end of day 18. This means 43 bins were loaded in each cell every other day. The profile of such loading scheme is shown in Fig. 1. In the batch-wise loading, a single cell was loaded to its capacity at a time, as the produce arrives. That is, all the 430 bins were loaded to a single cell and the next batches were loaded to the next cell and so forth until all the cells were filled. The products were assumed to be introduced at a uniform temperature of 10 8C and all the cells were at the set point temperature of 1 8C. In both cases, the ULO operation starts when a cell is full and 2 days after storage product temperature is attained. This means that for the step-wise loading the operation will start at almost the same time for all the cells. While for the batch-wise loading, each cell was ‘burned’ as the above stated conditions were fulfilled for that cell. For comparison purposes, cost of energy of refrigeration will be calculated using the following formula.
Crefr Z
qp hCOP
(1)
where Crefr is the cost of refrigeration, V tonK1; q, the chilling and storage heat load per ton of stored produce, kJ tonK1; p, the price of electricity (Z0.08 V kWhK1) and hCOP the coefficient of performance of the whole refrigeration plant which was assumed to be 2.51. Moreover, in order to quantify the firmness loss of the apples in terms of money, the grading system as shown in Fig. 2 was defined. This was based on the ‘Belgische Fruitveiling’ quality grading system for Jonagold apples [3]. In this grading, the symbols A1, A2, A3 and A4 represent apples with 50, 33, 25 and 20% red colour, respectively. On the other hand, the CC, C, Y and R represent the base (background) colour and the firmness of the fruit, CC being the extremely green base and extremely firm apple and R the yellow and soft apple. The simulation was carried out on a Celeron 366 Mhz, 128 Mb Ram personal computer with Microsoft Windows NT OS. 1 This value was estimated from data obtained from 5 refrigeration installations for fruit and vegetables in Belgium. The value corresponds to the one given in Hasse et al. [2] (Z2.56).
H.B. Nahor et al. / International Journal of Refrigeration 28 (2005) 471–480
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Fig. 2. Quality grading system based on appearance and firmness (A1, at least 50% red colour; A2, at least 33% red colour; A3, at least 25% red colour; A4, at least 20% red colour; CC, extremely green base and extremely firm; C, greenish-yellow and firm; Y, yellow; R, ripe).
3. Results 3.1. Effect of loading strategy on product quality 3.1.1. Product cooling rate In Fig. 3, the product temperature profiles and their respective room air temperatures are depicted for the two loading strategies. For the case of the full load condition
(batch-wise loading), the air temperature was observed to rise, since the load imposed by the product was higher than the available cooling capacity of the specific cell. On the other hand, for the step-wise loading (i.e. 10% load condition) the effect of the produce was barely evident on the room temperature. Obviously, slower product cooling rate is expected in the fully loaded condition. However, in comparison, in cooling 10% of the full load condition the
Fig. 3. (a) Typical product profiles in one of the cells of the batch-wise and step-wise loading conditions (b) corresponding room air temperatures (BW, batch-wise; SW, step-wise).
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Fig. 4. (a) Accumulated weight loss of the batch-wise and the step-wise loading conditions and (b) their respective room air relative humidity’s (BW, batch-wise; SW, step-wise).
apparent gain in cooling rate was only less than 50%. One reason for this could be that the heat transfer coefficient was considered constant and the same in both cases. Nevertheless, due to higher flow resistance in the fully loaded condition, a smaller heat transfer coefficient is expected resulting in slower cooling rate that the observed one. Another reason is the heat transfer limitation; i.e. even
though there was extra cooling capacity able to take more heat load in the case of the partially loaded condition, the rate of heat removal from the product was the limiting factor. Thus, the control system tries to keep the room temperature at the required set point by controlling the cooling capacity via the control valve. It should be noted here that the cooling rate obtained for the full load condition
Fig. 5. (a) Firmness loss of stored produce of the first cell of the batch-wise and in one of the cells of the step-wise loading schemes (b) their respective respiratory gas concentration profiles (BW, batch-wise; SW, step-wise).
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Fig. 6. (a) Heat load on the refrigeration plant with 10 ULO cells for batch-wise and the step-wise loading conditions and (b) their respective product heat load (BW, batch-wise; SW, step-wise).
was comparatively more dependent on the available capacity of the individual evaporators in each room. Nonetheless, from the product point of view the step-wise loading appears to be advantageous as it provides faster cooling. 3.1.2. Product weight loss The accumulated weight loss and their respective room air relative humidity are depicted in Fig. 4 for both strategies. A higher rate of moisture loss was observed for the full load condition at the beginning, because of low room air relative humidity as well as high temperature. Once at steady state, a constant rate moisture loss was attained. On the other hand, for the step-wise loading, a slower rate of moisture loss was observed at the beginning. However, due to the reduced relative humidity, each time product was loaded, a higher rate of moisture loss was observed until the end of product loading and stabilizes to a constant rate similar to the full load condition. When extended to a storage period of 180 days, 2.5 and 2.3% cumulative moisture loss was predicted for the step-wise and batch-wise loading strategies, respectively. This slight difference, which can hardly be explained, was evidently much less than the prediction accuracy of the employed models. These values correspond with the reported moisture loss of 2.5– 3.0% [4] in practice for Jonagold apples. No apparent advantage of one strategy over the other was observed in relation to moisture loss, as both stabilize to the same rate of moisture loss. 3.1.3. Product firmness loss Fig. 5 shows the firmness loss of the stored produce of
the two loading strategies and their respective room air concentrations of the respiratory gases. The advantage of the batch-wise over the step-wise loading was that once the room was full and once it attains the product set point temperature the ‘burning’ operation can start (in practice this is about 5–10 days after filling depending on the size of the cool cells). While in the step-wise loading, the ‘burning’ operation cannot start until all the cells were full. The observed difference in firmness was the maximum that can occur, that is the difference in firmness between the first batch loaded in the batch-wise and step-wise schemes. This difference closes to zero for the last batch, as in both cases the ‘burning’ operation started at about the 25th day after the start of loading of the first batch. It should be noted that the step-wise loading may result in difference in quality attributes between the first loaded and the last loaded product due to the longer time the first loaded product spends under atmospheric air with respect to the last loaded product. Overall, considering the whole produce and the biological variability (and thus the accuracy of the model) the observed difference may not be significant. However, as it is proven that fruit stored under optimal respiratory gas concentrations exhibit higher firmness than in normal air, this difference may be significant when lengthy step-wise loading periods are considered. Hence, it might be interesting to quantify the financial implication of the observed difference. At storage time of 120 days (4 months), the firmness of the first batch of the batch-wise-loaded fruit was around 60 N and that of the step-wise-loaded, 57 N. It is evident that there is a strong correlation between the firmness and the background colour of the fruit. Hence,
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Fig. 7. (a) Cumulative cooling and storage heat for the batch-wise and the step-wise loading conditions and (b) their respective cost per ton of product (BW, batch-wise; SW, step-wise).
there is a fair chance that these fruit lie in different quality groups, for instance in the high price category (500– 550 V tonK1) and the second level price category (450– 475 V tonK1) (Fig. 2). This means a difference in selling price of 50–75 V per ton. Assuming a cool cell contained 130 tons of fruit an apparent loss of 6500 V per cell is incurred. 3.2. Effect of loading strategy on mechanical plants’ performance/design 3.2.1. Refrigeration unit The effect of the product loading strategy on the refrigeration unit is depicted in Fig. 6(a). In the case of the batch-wise product loading condition, the heat load imposed on the refrigeration unit was delivered from the cell(s) that were packed to their capacity (product heat load and losses) and the rest of the cells to maintain their room air temperature set point (losses). In this case, less load fluctuation was observed as compared to the step-wise loading scheme where the heat load comes from the 10 partially filled rooms. This is because of the fact that in the case of step-wise loading, instantaneous high capacity requirement was demanded from all the cells operating in parallel while the plant was left idle on other instances. The major heat load was imposed by the product field heat (latent and sensible) and respiration heat. In Fig. 6(b), the product heat load component is shown for the two cases. In the case of the batch-wise loading condition, a peak and drastic decrease was observed at each loading event. This drastic decrease at the beginning was caused by the heating
up of the room temperature, thus decreasing the driving force, and resulting in a lower heat load. Subsequently, a slower rate of decrease was observed as compared to the step-wise loading condition. In order to estimate the energy usage by the refrigeration unit, the area under the heat load curves (Fig. 6(a)) was calculated. This is depicted in Fig. 7(a) where the evolution of the cumulative energy usage during the storage period is shown. This comparison shows that operationally the step-wise loading appears to have marginal lower energy cost. In Fig. 7(b) the cost of cooling and storing is shown per ton of produce. In the case of batchwise loading, it was observed that there was a large increase in the cost per ton of produce at the beginning and afterwards a gentle increase as more and more products were loaded. This shows that the load imposed on the refrigeration unit at the beginning was higher compared to the step-wise loading. It was observed that if extrapolated to 180 days of storage, there exists only a marginal difference (!1 V tonK1) in the total cost to cool and store 1 metric ton of produce (Jonagold apple in this case), which amounts to G45 V tonK1. From the design point of view, the available refrigeration capacity for the step-wise loading scheme was not efficiently used, as the plant remains idle for most of the time and thus operating at sub-optimal conditions. This scenario was caused by the excess capacity of the individual evaporators (coolers) in the cool cells, which suggests a smaller capacity coolers could be proposed implying smaller capacity refrigeration plant as a whole. On the other hand, in the case of the batch-wise loading, though the unit was not idling, the maximum capacity could not be
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Fig. 8. Individual cell refrigeration loads for (a) batch-wise loading and (b) step-wise loading conditions (BW, batch-wise; SW, step-wise).
utilized. Given that the cooling rate depicted in Fig. 3(a) is to be maintained, the individual capacity of the cooler should be retained. As can be read from Fig. 8(a), during the first 2 days the maximum capacity of the cooler was employed. However, since the load was distributed in time, taking only one cell at a time, again, implies a smaller capacity refrigeration unit.
3.2.2. Gas handling unit 3.2.2.1. Nitrogen generation unit. In Fig. 9, the nitrogen requirement for the ‘burning’ operation in each case of the loading schedule with their respective oxygen concentration profiles is shown. It is to be recalled that the nitrogen generator was attached to a buffer tank from where the
Fig. 9. (a) Nitrogen requirement in batch-wise product loading for 10 cells (b) nitrogen requirement in step-wise loading for 10 cells (c) respective oxygen profiles in the first cell of the batch-wise scheme and in one of the cells in the step-wise scheme (BW, batch-wise; SW, stepwise).
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Fig. 10. (a) Rate of carbon dioxide production for batch-wise and step-wise product loading (b) respective carbon dioxide profiles in the first cell of the batch-wise scheme and in one of the cells in the step-wise scheme (BW, batch-wise; SW, step-wise).
injection to the rooms was carried out. Since, in the case of the step-wise loading the cells are set into ULO operation at the same time, the load on the nitrogen generator is obviously, 10 times (from the 10 cells) higher as compared to batch-wise product loading schedule. In reality, the assumption that the cells get filled at the same time is hardly met. Thus, for instance, the requirement in the step-wise loading can be reduced by 50% if the burning operation of the cells was scheduled in an interval of 10 h instead of at the same time. This means that the ‘burning’ of the last room will start 90 h after the first one (injection time of less than 50 h for each cell as can be seen from Fig. 9(c)), which might have slight but not considerable effect on the firmness loss of the fruit. This shows that the way the cool rooms are loaded has substantial implication on the size of the equipment and thus the investment cost. In design considerations, these two situations depict the required minimum and maximum nitrogen generation capacity and, hence, the demand for nitrogen generation falls between these limits. Practically, the capacity of the nitrogen generation unit is determined by optimising the gas flow rate (99–97% nitrogen) to the cells and the time required to pull down the oxygen concentration to less than 5% (typically 30–50 h). This depends on the size and capacity of the cells, the type of produce and the loading situations. Thus, for a given cell capacity, the optimal gas flow rate and pull-down time, and thus the size of the unit, can be determined by simulating different loading situations and by using various produce using the present tool. 3.2.2.2. Carbon dioxide scrubber. In Fig. 10, the rate of carbon dioxide production in the 10 cells and the respective carbon dioxide concentration profiles for the two product
loading schedules is shown. It is observed that at the end of the loading period (day 20) there exists a difference of more than 120 kgCO2 dK1 in rate of carbon dioxide production. At this moment, more than half of the batch-wise-loaded cells already attained the set point concentrations of the respiratory gases where the rate of respiration was more than 70% slower. When the step-wise-loaded cells were set into ULO operation (day 25), a drastic decrease in carbon dioxide production was observed before it stabilized to the value of 73 kgCO2 dK1 that is the value obtained in batchwise loading, as can be read from Fig. 10(a). This rate was the load the scrubber unit should remove from the cells during the storage period of the fruit. During the transient state of the respiratory gas concentrations, it was observed that for the step-wise loading (cells operate in a parallel mode), a single scrubber could not handle the load causing a long queue and, as a result, considerable build up of carbon dioxide in the cells. Thus, the results as shown in Fig. 10 was obtained by using two scrubbers for the step-wise loading scheme, handling five cells each. However, during the steady state the scrubbers were idling most of the time. In the case of the batch-wise charging, only one scrubber could handle the load with a maximum of three cells being in the queue at times. This means a maximum delay of 40 min (the scrubber needs 20 min per cell, 10 min for scrubbing and 10 for regenerations) and without substantial effect on the concentration of the carbon dioxide in the cool cells. When the total amount of operation time of the scrubber was calculated, it was found 300.67 and 200.33 h (for both scrubbers) for the batch-wise and step-wise schemes, respectively, during the storage period of 40 days. This is understandably so because in the case of batch-wise loading
H.B. Nahor et al. / International Journal of Refrigeration 28 (2005) 471–480 Table 1 Summary of findings Loading strategy Batch-wise Step-wise Product quality (firmness) Energy cost Investment cost
Individual evaporators Gas handling unit
Higher quality product Marginally higher Higher capacity Lower capacity
Lower quality product Marginally lower Lower capacity Higher capacity
the scrubber started working at much earlier time (day 7 as compared to day 25) and was operating one at a time and maintaining the rooms at the set point. This means that during the transient period, the batch-wise scheme incurs higher operational cost. However, it should be emphasised that the cost incurred by the whole gas-handling unit is insignificant as compared to the refrigeration cost (For instance, the adsorbed kW of a unit that can handle 300 kgCO2 dK1 is about 1.8. This means that for the 300.67 h of operation during 40 days, a cost of 0.32 V tonK1 is incurred).
4. Discussion and conclusion As an application of the global CA cool storage model, the implication of some practical operational issues (related to product loading strategies) to the product quality, mechanical plants performance and design aspects was investigated. The summary of the findings are summarized in Table 1. With regards to product quality, it was found that no difference in cumulative moisture loss was observed. However, the batch-wise product loading was apparently advantageous with regards to firmness loss of the produce. In general, the influence of the considered product loading schemes on the product quality was apparent. Concerning the refrigeration plant, less load variation was observed in the batch-wise loading but it was marginally energy intensive over the step-wise product loading strategy. Considering sizing of the refrigeration plant, the batchwise scheme required high capacity individual evaporators as compared to the step-wise loading. However, overall, the capacity of the refrigeration unit is not affected by the product-loading scheme. During transient situations, higher plant capacity was required for the gas-handling unit (nitrogen generator and carbon dioxide scrubber) in the case of the step-wise loading scheme, due to parallel working mode of the cells. Nevertheless, 33% higher operational time was required for the carbon dioxide scrubber when batch-wise product loading strategy was used. In practice, operational and market situations and other
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aspects are considered, where the decision to go for either one of the strategies is based upon. This has to do with the freedom of decision the growers stipulate as to when to sell their produce. When this flexibility is required, the step-wise loading strategy is ideal because (i) the cell(s) a grower hires are not shared and he can load them at his own picking rate and (ii) does not interfere with other growers’ decision and can sell his produce at his own choosing when he thinks the price is profitable. On the other hand, when collective decisions drive the market the batch-wise loading is preferred because of its simplicity in scheduling the available storage space and somewhat easier operational condition with respect to loading of the product. The light this investigation sheds on these choices is that at what cost, besides the operational conditions and flexibility, are these decisions made. From this perspective, the decision can be weighed in its multi-facets of product quality, plant performance and operational and market conditions. Based on that, other loading strategies can be designed which for instance consider the combination of the two strategies, achieved by allocating some of the available storage space to step-wise and the rest to batch-wise product-loading strategies. In conclusion, it was demonstrated that the global CA cool storage model could be used for sizing (design) and evaluation of plant performance taking into consideration the implication on the product quality. Other areas of application include development of new control strategies and tuning of controller parameters and model based product control. Concerning the latter, since sensor based automatic control of product quality can prove to be difficult, the control of the ‘a priori’ known optimal set points can be assisted by model based product quality control. In this case, the response from the quality models is fed to the controller for better maintenance of the microenvironment for overall plant optimal operation and increased final product quality.
Acknowledgements The IDO/00/008 project and the IRO scholarship (KULeuven) are acknowledged for financial assistance. Authors N. Scheerlinck and P. Verboven are postdoctoral fellows with the Flemish Fund for Scientific Research (F.W.O.—Vlaanderen).
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[4] E.A. Veraverbeke, P. Verboven, P. Van Oostveldt, B.M. Nicolai, Prediction of moisture loss across the cuticle of apple (Malus sylvestris subsp. mitis (Wallr.)) during storage. Part 2. Model simulations and practical applications, Postharvest Biol Technol 30 (1) (2003) 89–97.